Computer vision researchers have long sought efficient methods for segmenting discrete objects in an image while limiting the effects of both bias and variance. Conventional methods of image segmentation implement various image partition algorithms in order to segment out an image along contours extracted from the image at question. There are a number of standard algorithms which may be used to identify such contours, which typically focus on identifying pixel differences or discontinuities.
However, there are many known flaws with contemporary image segmentation techniques when used for identifying object boundaries on an image. For example, some image segmentation techniques often fail to accurately detect the complete boundary of an object, or a region's edge. Furthermore, standard techniques for edge detection (e.g., Canny Edge Detection) often fail to determine the full edge length on a shape when confronted with complex edges. The end result is that one must “piece together” a multitude of straight line segments to reconstruct the complex edge of an object. This process is further complicated when minor variations in pixel shading or vibrancy result in a significant number of discontinuities along an edge. Thus, modern edge detection methods fail by identifying false or incomplete edge patterns, which must then be manually rejected.
Some popular contemporary methods of image segmentation utilize machine learning, probability analysis, and expert guided learning. However, each of these methods suffers from both well-known bias and/or variance related errors. Moreover, such methodologies are often time consuming and computationally expensive. Thus, more accurate, efficient, and reliable image segmentation techniques are needed.